Tone Analysis: A Graduate School Technique in Predicting Student Feedback

Abra State Institute of Sciences and Technology
Bangued, Abra, Philippines

Abra State Institute of Sciences and Technology
Lagangilang, Abra, Philippines


Getting student’s feedback is very important because it can provide insights on course offerings, quality teaching, subject contents, and student experiences to ensure effective delivery of services. Specifically, It achieved the following objectives: 1) Determine the activities of students as reflected in Computer Education Syllabus; 2) Determine the tone of experiences in student activities along tentative, fear, anger, joy, analytical and confident; and 3) Develop a recommender system for syllabus revisit.

The data were analyzed along tentative, fear, anger, joy, analytical and confident tones of experiences. These tones of experiences are grouped into negative and positive. Negative tones include tentative, fear, and anger while positive tones are joy, analytical and confident.

Analytical tone is dominant in all the eleven activities wherein, activities 2 (MS Excel), 4 (MPEG4 MS PowerPoint), and 7 (Creating Flyer) got the most frequency of tone of 20, 22, 21 respectively. The study also found that the Tentative tone as negative experience appeared in all the eleven activities and only activity 9 on designing and creating Tarpaulin using MS Publisher got the highest frequency. Sad tone existed only in activities 3 and 9 on creating slides, inserting objects and animations using MS PowerPoint and designing and creating Tarpaulin. The activities 1, 3, 5, 6, 8, 9 and 10 got almost the same frequency of 15 to 19, while activity 11 (Video conversion using VLC) got the least frequency of 14. 

 “Analytical” tone represents the overall positive feedback of the students. “Tentative” tone as negative appeared in all the 11 activities while “Sad” tone only existed in Activities 3 and 9. Student experiences are a rich source for extracting student feedback. It can be used to determine whether an activity in a Syllabus is to be retained or to be replaced


Tone analysis, text mining, student’s feedback, recommender system


Harvey, L. (2003). Student feedback. Quality in higher education, 9(1), 3-20.

Hunter, L. (2012). Challenging the reported disadvantages of e-questionnaires and addressing methodological issues of online data collection. Nurse researcher, 20(1), 11.

Garland, E.L., Fredrickson, B., Kring, A.M., Johnson, D.P. Meyer, P.S., & Penn, D. L. (2010). Broaden-and-Build Theory. Clinical psychology review. 30(7), 849-864.

Rashid, A., Asif, S., Butt, N. A., & Ashraf, I. (2013). Feature level opinion mining of educational student feedback data using sequential pattern mining and association rule mining. International Journal of Computer Applications, 81(10).

Plapinger, T. (2017). A Beginners Guide to Natural Language Processing. Retrieved from

Shetty, B. (2018). Natural Language Processing (NLP) for Machine Learning. Retrieved from

Sas. (2019). Natural Language Processing what it is and why it matters. Retrieved from

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27-34.

Hearst, M. (2003). What is text mining? SIMS, UC Berkeley.

Hotho, A., Nürnberger, A., & Paaß, G. (2005, May). A brief survey of text mining. In Ldv Forum (Vol. 20, No. 1, pp. 1962).

Abd-Elrahman, A., Andreu, M., & Abbott, T. (2010). Using text data mining techniques for understanding free-style question answers in course evaluation forms. Research in Higher Education Journal, 9, 1.

IBM Watson Tone Analyzer. (2019). Tone Analyzer. Retrieved from